From first pilot to production scale. Practical guides for making AI actually work in your organization—not just impressive demos.
Selecting the right pilot, scoping for success, the 90-day plan, and making the scale/kill decision.
Why data is the real work. Quality assessment, cleaning strategies, privacy, and pipelines.
Getting better outputs. Basic patterns, advanced techniques, building prompt libraries.
Why pilots don't scale, the platform approach, and center of excellence model.
Defining requirements, evaluation criteria, POC best practices, contract negotiation.
Architecture options, API-first, legacy system integration, security considerations.
Why AI testing is different. Test data strategy, accuracy metrics, edge cases, bias testing.
Monitoring, observability, model drift detection, retraining strategies, cost optimization.
The top 10 failures and how to avoid them. Technical, organizational, and vendor pitfalls.
Leading vs lagging indicators. Technical, business, and adoption metrics. Building dashboards.